Sextant: Grab's Scalable In-Memory Spatial Data Store for Real-Time K-Nearest Neighbour Search

The gas alarms based on sensors are widely used, but there are still some limitations. By design a gas real time monitoring system, the gas alarms within a certain range transport collected data to a remote computer via wired or wireless method. Remote computer can monitoring the gas environment by receiving and monitoring alarms’ collected data. In order to ensure system’s reliability, transmission mechanism based on handshake is used. Data compression technology also included to reduce the storage space required by data store.

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Estimating productivity of earthmoving operations using spatial technologies1This paper is one of a selection of papers in this Special Issue on Construction Engineering and Management.

Canadian Journal of Civil Engineering ◽

10.1139/l2012-059 ◽

2012 ◽

Vol 39 (9) ◽

pp. 1072-1082 ◽

Cited By ~ 9

Author(s):

Ali Montaser ◽

Ibrahim Bakry ◽

Adel Alshibani ◽

Osama Moselhi

Keyword(s):

Real Time ◽

Spatial Data ◽

Data Fitting ◽

Google Earth ◽

Simulation Software ◽

Distribution Functions ◽

Automated Classification ◽

Cycle Times ◽

Automated Method ◽

Spreadsheet Application

This paper presents an automated method for estimating productivity of earthmoving operations in near-real-time. The developed method utilizes Global Positioning System (GPS) and Google Earth to extract the data needed to perform the estimation process. A GPS device is mounted on a hauling unit to capture the spatial data along designated hauling roads for the project. The variations in the captured cycle times were used to model the uncertainty associated with the operation involved. This was carried out by automated classification, data fitting, and computer simulation. The automated classification is applied through a spreadsheet application that classifies GPS data and identifies, accordingly, durations of different activities in each cycle using spatial coordinates and directions captured by GPS and recorded on its receiver. The data fitting was carried out using commercially available software to generate the probability distribution functions used in the simulation software “Extend V.6”. The simulation was utilized to balance the production of an excavator with that of the hauling units. A spreadsheet application was developed to perform the calculations. An example of an actual project was analyzed to demonstrate the use of the developed method and illustrates its essential features. The analyzed case study demonstrates how the proposed method can assist project managers in taking corrective actions based on the near-real-time actual data captured and processed to estimate productivity of the operations involved.

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HiBuffer: Buffer Analysis of 10-Million-Scale Spatial Data in Real Time

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7120467 ◽

2018 ◽

Vol 7 (12) ◽

pp. 467 ◽

Cited By ~ 3

Author(s):

Mengyu Ma ◽

Ye Wu ◽

Wenze Luo ◽

Luo Chen ◽

Jun Li ◽

...

Keyword(s):

Real Time ◽

Spatial Data ◽

High Performance ◽

Large Scale ◽

Computation Time ◽

Buffer Analysis ◽

Data Volume ◽

Time Buffer ◽

Real World Datasets ◽

Spatial Indexes

Buffer analysis, a fundamental function in a geographic information system (GIS), identifies areas by the surrounding geographic features within a given distance. Real-time buffer analysis for large-scale spatial data remains a challenging problem since the computational scales of conventional data-oriented methods expand rapidly with increasing data volume. In this paper, we introduce HiBuffer, a visualization-oriented model for real-time buffer analysis. An efficient buffer generation method is proposed which introduces spatial indexes and a corresponding query strategy. Buffer results are organized into a tile-pyramid structure to enable stepless zooming. Moreover, a fully optimized hybrid parallel processing architecture is proposed for the real-time buffer analysis of large-scale spatial data. Experiments using real-world datasets show that our approach can reduce computation time by up to several orders of magnitude while preserving superior visualization effects. Additional experiments were conducted to analyze the influence of spatial data density, buffer radius, and request rate on HiBuffer performance, and the results demonstrate the adaptability and stability of HiBuffer. The parallel scalability of HiBuffer was also tested, showing that HiBuffer achieves high performance of parallel acceleration. Experimental results verify that HiBuffer is capable of handling 10-million-scale data.

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Geographic Information and Geo-visualisation in support of Disaster Resilience

Proceedings of the ICA ◽

10.5194/ica-proc-2-68-2019 ◽

2019 ◽

Vol 2 ◽

pp. 1-8

Author(s):

Edward Kurwakumire ◽

Paul Muchechetere ◽

Shelter Kuzhazha ◽

Guy Blachard Ikokou

Keyword(s):

Real Time ◽

Spatial Data ◽

Disaster Preparedness ◽

Geographic Information ◽

Public Access ◽

Data Sets ◽

Disaster Resilience ◽

Data Systems ◽

Information Infrastructures ◽

Timely Access

Abstract. Society continues to become more spatially enabled as spatial data becomes increasingly available and accessible. This is partly due to democratisation of data achieved through open access of framework data sets. On the other hand, mobile devices such as smartphones have become more accessible, giving the public access to applications that use spatial data. This has tremendously increased the consumption of spatial data at the level of the general public. Spatial data has a history in planning and decision making as detailed in literature on promises and benefits of geographic information. We extend these promises to sustainability and disaster resilience. It is our belief that geographic information (GI) and geographic information infrastructures (GIIs) contribute positively towards the achievement of sustainability in cities and nations and in disaster resilience. This study carries out a review of geo-visualisation and GI applications in order to determine their suitability and impact in disaster resilience. Real-time GI are significant for cities to ensure sustainability and to increase disaster preparedness. Geographic information infrastructures need to be integrated with BIG data systems to ensure that local government agencies have timely access to real time geographic information so that decisions on sustainability and disaster resilience can be effectively done.

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Integrating Drone Technology into an Innovative Agrometeorological Methodology for the Precise and Real-Time Estimation of Crop Water Requirements

Hydrology ◽

10.3390/hydrology8030131 ◽

2021 ◽

Vol 8 (3) ◽

pp. 131

Author(s):

Stavros Alexandris ◽

Emmanouil Psomiadis ◽

Nikolaos Proutsos ◽

Panos Philippopoulos ◽

Ioannis Charalampopoulos ◽

...

Keyword(s):

Real Time ◽

Spatial Data ◽

Precision Agriculture ◽

Time Estimation ◽

Stress Index ◽

Crop Water ◽

Crop Water Stress Index ◽

Water Requirements ◽

Ongoing Research ◽

Ir Camera

Precision agriculture has been at the cutting edge of research during the recent decade, aiming to reduce water consumption and ensure sustainability in agriculture. The proposed methodology was based on the crop water stress index (CWSI) and was applied in Greece within the ongoing research project GreenWaterDrone. The innovative approach combines real spatial data, such as infrared canopy temperature, air temperature, air relative humidity, and thermal infrared image data, taken above the crop field using an aerial micrometeorological station (AMMS) and a thermal (IR) camera installed on an unmanned aerial vehicle (UAV). Following an initial calibration phase, where the ground micrometeorological station (GMMS) was installed in the crop, no equipment needed to be maintained in the field. Aerial and ground measurements were transferred in real time to sophisticated databases and applications over existing mobile networks for further processing and estimation of the actual water requirements of a specific crop at the field level, dynamically alerting/informing local farmers/agronomists of the irrigation necessity and additionally for potential risks concerning their fields. The supported services address farmers’, agricultural scientists’, and local stakeholders’ needs to conform to regional water management and sustainable agriculture policies. As preliminary results of this study, we present indicative original illustrations and data from applying the methodology to assess UAV functionality while aiming to evaluate and standardize all system processes.

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Digital matter as interdisciplinary commodity

The Journal of Public Space ◽

10.5204/jps.v2i3.119 ◽

2017 ◽

Vol 2 (3) ◽

pp. 103

Author(s):

Uwe Rieger

Keyword(s):

Real Time ◽

Spatial Data ◽

Architectural Design ◽

Large Scale ◽

Mixed Reality ◽

Digital Fabrication ◽

Digital Information ◽

Design Environment ◽

Digital World ◽

Starting Point

With the current exponential growth in the sector of Spatial Data Technology and Mixed Reality display devises we experience an increasing overlap of the physical and digital world. Next to making data spatially visible the attempt is to connect digital information with physical properties. Over the past years a number of research institutions have been laying the ground for these developments. In contemporary architecture architectural design the dominant application of data technology is connected to graphical presentation, form finding and digital fabrication. The arc/sec Lab for Digital Spatial Operations at the University of Auckland takes a further step. The Lab explores concepts for a new condition of buildings and urban patterns in which digital information is connected with spatial appearance and linked to material properties. The approach focuses on the step beyond digital re-presentation and digital fabrication, where data is re-connected to the multi-sensory human perceptions and physical skills. The work at the Lab is conducted in a cross disciplinary design environment and based on experiential investigations. The arc/sec Lab utilizes large-scale interactive installations as the driving vehicle for the exploration and communication of new dimensions in architectural space. The experiments are aiming to make data “touchable” and to demonstrate real time responsive environments. In parallel they are the starting point for both the development of practice oriented applications and speculation on how our cities and buildings might change in the future. The article gives an overview of the current experiments being undertaken at the arc/sec Lab. It discusses how digital technologies allow for innovation between the disciplines by introducing real time adaptive behaviours to our build environment and it speculates on the type of spaces we can construct when digital matter is used as a new dynamic building material.

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Development of geoportal for landslide monitoring

Glasnik srpskog geografskog drustva ◽

10.2298/gsgd1204063s ◽

2012 ◽

Vol 92 (4) ◽

pp. 63-78

Author(s):

Dubravka Sladic ◽

Milan Vrtunski ◽

Ivan Alargic ◽

Aleksandra Ristic ◽

Dusan Petrovacki

Keyword(s):

Real Time ◽

Spatial Data ◽

Service Oriented Architecture ◽

Reference Value ◽

Landslide Monitoring ◽

Alert System ◽

Service Oriented ◽

Data Infrastructures ◽

Spatial Data Infrastructures

The paper presents the implementation of geoportal for landslide monitoring which which includes two subsystems: a system for acquisition, storage and distribution of data on landslides and real time alert system. System for acquisition, storage and distribution of data on landslides include raster and vector spatial data on landslides affected areas, as well as metadata. Alert system in real time is associated with a sensor for detecting displacement, which performs constant measurements and signals in case of exceeding the reference value. The system was developed in accordance with the standards in the field of GIS: ISO 19100 series of standards and OpenGIS Consortium and is based on service-oriented architecture and principles of spatial data infrastructures.

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Providing Near Real-Time Traffic Information within Spatial Data Infrastructures

2009 International Conference on Advanced Geographic Information Systems & Web Services ◽

10.1109/geows.2009.17 ◽

2009 ◽

Cited By ~ 2

Author(s):

Christian Mayer ◽

Beate Stollberg ◽

Alexander Zipf

Keyword(s):

Real Time ◽

Spatial Data ◽

Traffic Information ◽

Real Time Traffic ◽

Data Infrastructures ◽

Spatial Data Infrastructures

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A Real Time Topological Map Matching Methodology for GPS/GIS-Based Travel Behavior Studies

Transportation Systems and Engineering ◽

10.4018/978-1-4666-8473-7.ch020 ◽

2015 ◽

pp. 422-439

Author(s):

Carola A. Blazquez ◽

Pablo A. Miranda

Keyword(s):

Real Time ◽

Spatial Data ◽

Travel Behavior ◽

Map Matching ◽

Gps Measurements ◽

Case Scenario ◽

Topological Map ◽

Gis Environment ◽

Control And Management ◽

Parameter Values

The map matching problem arises when GPS measurements are incorrectly assigned to the roadway network in a GIS environment. This chapter presents a real-time topological decision rule-based methodology that detects and solves spatial mismatches as GPS measurements are collected. A real-time map matching methodology is required in several applications, such as fleet management, transit control and management, and travel behavior studies, in which decision-making must be performed simultaneously with the movement of vehicles, individuals, or objects. A computational implementation in a real case scenario in Chile indicates that the algorithm successfully resolves over 96% of the spatial mismatches encountered in real time. Various algorithmic parameter values were employed to test the performance of the algorithm for data collected every 5 and 10 seconds. Overall, the algorithm requires larger buffer sizes and speed ranges to obtain better results with lower spatial data qualities.

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LDAP: Lightweight Dynamic Auto-Reconfigurable Protocol in an IoT-Enabled WSN for Wide-Area Remote Monitoring

Remote Sensing ◽

10.3390/rs12193131 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3131

Author(s):

Gazi M. E. Rahman ◽

Khan A. Wahid

Keyword(s):

Water Quality ◽

Real Time ◽

Spatial Data ◽

Remote Monitoring ◽

Water Quality Monitoring ◽

Sensor Nodes ◽

Quality Monitoring ◽

Wide Area ◽

Long Distance ◽

Mobile Data

IoT (Internet of Things)-based remote monitoring and controlling applications are increasing in dimensions and domains day by day. Sensor-based remote monitoring using a Wireless Sensor Network (WSN) becomes challenging for applications when both temporal and spatial data from widely spread sources are acquired in real time. In applications such as environmental, agricultural, and water quality monitoring, the data sources are geographically distributed, and have little or no cellular connectivity. These applications require long-distance wireless or satellite connections for IoT connectivity. Present WSNs are better suited for densely populated applications and require a large number of sensor nodes and base stations for wider coverage but at the cost of added complexity in routing and network organization. As a result, real time data acquisition using an IoT connected WSN is a challenge in terms of coverage, network lifetime, and wireless connectivity. This paper proposes a lightweight, dynamic, and auto-reconfigurable communication protocol (LDAP) for Wide-Area Remote Monitoring (WARM) applications. It has a mobile data sink for wider WSN coverage, and auto-reconfiguration capability to cope with the dynamic network topology required for device mobility. The WSN coverage and lifetime are further improved by using a Long-Range (LoRa) wireless interface. We evaluated the performance of the proposed LDAP in the field in terms of the data delivery rate, Received Signal Strength (RSS), and Signal to Noise Ratio (SNR). All experiments were conducted in a field trial for a water quality monitoring application as a case study. We have used both static and mobile data sinks with static sensor nodes in an IoT-connected environment. The experimental results show a significant reduction (up to 80%) of the number of data sinks while using the proposed LDAP. We also evaluated the energy consumption to determine the lifetime of the WSN using the LDAP algorithm.

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